Use of three-dimensional acceleration sensing to assess dairy cow gait and the effects of hoof trimming
Bibliographic record
Abstract
The aim of this study was to assess the use of three-dimensional acceleration sensing to describe the gait of dairy cows and the effect of hoof trimming. In Trial 1, a three-dimensional acceleration sensor was attached to the carpal region of a front leg of six Holstein cows who were then walked for 9 m. Results showed that measures of acceleration could clearly show the presence of steps, which were divided into an acceleration phase, which involved the hoof descending to and ascending from the ground, and a non-acceleration phase, which included the swing phase. Lissajous figures could succinctly depict the degree of acceleration for each cow and showed that one cow who was lame walked with greater vertical and lateral acceleration than the remaining cows. In Trial 2, 17 loose-housed Holstein cows were walked for 20 m during the month before, once during the month after and once 2 months after hoof trimming with the sensor attached at the end of their thoracic vertebrae to measure acceleration of the whole body. To relate the acceleration of the body to movements of the legs, image analysis was used to the range of vertical movement of four major skeletal joints, using visual markers attached to them. Hoof trimming significantly (P < 0.005) decreased both the range of vertical movement of the joints of front and hind legs and the variance of lateral and forward acceleration at the end of the thoracic vertebrae, suggesting improved gait pattern smoothness. Acceleration sensing was able to characterize variation among gait patterns of the cows and could reproduce the pattern shown by image analysis. This study demonstrates that three-dimensional acceleration sensing of either the carpal region of a front leg or the end of the thoracic vertebrae is useful to detect walking irregularities and evaluate the effectiveness of hoof trimming on walking ability of cattle.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".